But this approach usually introduces many irrelevant mentions, and it's very difficult to automatically determine the scope of discourse.

some recent work exploited more restricted measures by only choosing those mentions which are topically related (Cassidy et al., 2012;Xu et al., 2012), bear a relation from a fixed set (Cheng and Roth, 2013), coreferential (Nguyen et al., 2012;Huang et al., 2014), socially related (Cassidy et al., 2012;Huang et al., 2014), dependent (Ling et al., 2014), or a combination of these through meta-paths (Huang et al., 2014).


News and social media are source text genres that tend to focus on new information, trending topics, breaking events, or even mundane details about the entity.

the KB usually provides a snapshot summarizing only the entity's most representative and important facts.


(2013) with artificial paragraph tokens, which accumulate the meaning of words appearing in the respective paragraphs.

to these shallow methods, other approaches employ deep multi-layer networks for the processing of sentences.


Although attractive from this perspective, the kernel-based approach comes with a high computational cost.

to prior work, our approach effectively learns lowdimensional representation of words and their roles, eliminating the need for heavy manual feature engineering.


The predicates in each sentence are also given during both training and testing.

we neither predict nor use the sense for each predicate.


A micro-averaged accuracy measure is used so as not to disproportionately weigh short recipes.

in order to allow comparison to mean Kendall's Tau, commonly used in works on order learning, we further report a macroaveraged acc 2 by computing acc 2 for each recipe separately, and taking the average of resulting accuracy levels.


Derivations generated for these documents are such that both translation model features (with or without the LM) and retrieval features agree on a path close to the SMT Viterbi translation.

other relevant documents require more non-standard lexical choices that are harder to achieve in a +LM search space, since the strong weight on the language model, plus a language model-driven pruning technique, strongly favor lexical choices that agree with the language model's concept of fluency.


For evaluation of metrics that operate at the system or document-level such as BLEU, inconsistency in individual human judgments can, to some degree, be overcome by aggregation of individual human assessments over the segments within a document.

for evaluation of segment-level metrics, there is no escaping the need to boost the consistency of human annotation of individual segments.


For a sample mean for which the variance is known, the required sample size can be computed for a specified standard error.

due to the large number of distinct translations we deal with, the variance in sample score distributions may change considerably from one translation to the next.


The reconstruction of the copy history of manuscript texts is largely similar to that of DNA, which is why phylogenetic approaches have been adopted (Robinson and O'Hara, 1996;Robinson et al., 1998;van Reenen et al., 1996;van Reenen et al., 2004;Spencer et al., 2004;Roos and Heikkilä, 2009;Roelli and Bachmann, 2010;Andrews and Macé, 2013).

the main goal of the philological work on ancient manuscripts is not the reconstruction of the copy history but compiling an edition of a historical text.


This may be seen as a computer philological co-loan from bio-informatics.

it might correspond to a more Bédierian edition practise, where stemmatology is emphasized because it can point to the most important manuscript, which is however implicit and unlikely.


Bootstrapped classifiers iteratively generalize from a few seed examples or prototypes to other examples of target labels.

sparseness of language and limited supervision make the task difficult.


To train good word alignment models, we require access to a large parallel corpus.

collection of parallel corpora has mostly focused on a small number of widely-spoken languages.


They compare their automated reconstructions with the ones reconstructed by historical linguists and find that their model beats an edit-distance baseline.

their model has a requirement that the tree structure between the languages under study has to be known beforehand.


PLSA and LDA are the typical unsupervised topic models, that is non-knowledgeable model.

biterm topic model (bTM) (Yan et al., 2013) leverages self-contained knowledge into semantic analysis.


vast amount of lexical knowledge about words and their relationships, denoted as LR-sets, available in online dictionaries or other resources can be exploited by this model to generate more coherent topics.

for external knowledge-based models, the incorporated knowledge is too general to be consistent with the short text in the semantic space.


Here, in both plots, it is clearly shown that, as the frequency of nouns in the corpus increases our approach outperforms baselines for both Hindi and English WSD.

semCor baseline accuracy decreases for those words which occur more than 8 times in the test corpus.


due to time and space constraints we have performed our experiments on only Hindi and English languages.


When faced with a new domain, one option is to try to leverage available unlabeled data.

rather than resorting to pure self-training approaches (self-labeling), we here resort to another source of information.


Dev and test sets Our approach is basically parameter free.

we did experiment with different ways of extending Wiktionary and hence used an average over three English Twitter dev sections as development set (Ritter et al., 2011;Gimpel et al., 2011;Foster et al., 2011), all mapped and normalized following .


Our approach is similar to mining high-precision items.

previous approaches on this in NLP have mainly focused on well-defined classification tasks, such as PP attachment (Pantel and Lin, 2000;Kawahara and Kurohashi, 2005), or discourse connective disambiguation (Marcu and Echihabi, 2002).


It is thus verified that density peaks clustering algorithm is able to handle MDS effectively.


Efforts have been made to port the existing semantic annotation system to other languages (Finnish and Russian) (Löfberg et al., 2005;Mudraya et al., 2006), so a prototype software framework could be used.

manually developing semantic lexical resources for new languages from scratch is a time consuming task.


Recent developments in this area include Zhang and Rettinger's work (2014) in which they tested a toolkit for Wikipedia-based annotation (wikification) of multilingual texts.

in the work described here we employ a lexicographically-informed semantic classification scheme and we perform all-words annotation.


Compiled without professional editing, these bilingual word lists contain errors and inaccurate translations, and hence they introduced noise into the mapping process.

they provided wider lexical coverage of the languages involved and complemented the limited sizes of the high-quality dictionaries used in our experiment.


Our experiment demonstrates that, if appropriate high-quality bilingual lexicons are available, it is feasible to rapidly generating prototype systems with a good lexical coverage with our automatic approach.

our experiment also shows that, in order to achieve a high precision, parallel/comparable corpus based disambiguation is needed for identifying precise translation equivalents, and a certain amount of manual cleaning and improvement of the automatically generated semantic lexicons is indispensible.


The most intuitive way to integrate the similarities between terms is averaging them: (2) This similarity averages all the pairwise similarities between terms a i 's and b j 's.

we can expect a lot of the similarities φ(a i , b j ) to be close to zero.


For example, the suffix ed is generally indicative of the past tense in English.

distributional similarity has also been shown to be an important cue for morphology (Yarowsky and Wicentowski, 2000;Schone and Jurafsky, 2001).


(Prabhakaran et al., 2012) achieves a commendable accuracy in detecting overt display of "power".

by our definitions, this is a lower level attribute and is similar to authoritative behavior which is a lower level concept than Leadership or Status.


We assume the following ordering exists: authoritative behavior > motivational behavior > negative deference > positive deference in the opposite direction > closeness.

we do not assume such an ordering for the SC Leadership.


We achieved a very high recall (close to 1.0) for most indicators with these rules on test data.

in few cases, the frequency of such indicators (such as politeness) were very low deeming the set of regular expressions as incomplete.


Then, the probability of predicting the word w o given the word w i is defined as: wo) w∈V e ow i (w) This is referred as the softmax objective.

for larger vocabularies it is inefficient to compute o w i , since this requires the computation of a |V |×d w matrix multiplication.


It also shows that careful initialization of model parameters can bring further improvements.

we also find that words that are close to the centroid are not necessarily representative of what linguists consider to be prototypical.


The MPQA opinion annotated corpus Wilson, 2007) is entirely span-based, and contains no eTarget annotations.

it provides an infrastructure for sentiment annotation that is not provided by other sentiment NLP corpora, and is much more varied in topic, genre, and publication source.


The target is the event Rushdie insulting the Prophet.

the assertion that the Imam is negative toward the insult event is within the scope of this article.


The parallel computation of large number of small dense matrix operations (multiply, LU decomposition, triangular solve) is a perfect fit for implementation a GPU, which can achieve vastly more throughput than modern CPUs can.

using the CPU's neighborhood structure on the GPU has a crippling bottleneck.


In initial experiments, we found that when this approach is implemented on a GPU it is even slower than the CPU-based implementation.

memory bandwidth is extremely high on modern GPUs.


We find that both techniques of enhancing the lexical coverage of the semantic parsers result in improved parsing performance, and that the improvements add up nicely.

improved parsing performance does not correspond to improved F1-score in answer retrieval when using the respective parser in a response-based learning framework.


The most straightforward strategy to perform model selection for the task of response-based learning for SMT is to rely on parsing evaluation scores that are standardly reported in the literature.

as we will show experimentally, if precision is taken as the percentage of correct answers out of instances for which a parse could be produced, recall as the percentage of total examples for which a correct answer could be found, and F1 score as their harmonic mean, the metrics are not appropriate for model selection in our case.


Developing a system that can automatically respond to a user's utterance has recently become a topic of research in natural language processing.

most works on the topic take into account only a single preceding utterance to generate a response.


Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema.

current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on linguistic tools such as part-of-speech taggers and dependency parsers.


Possible inconsistencies are resolved by adjudication, and models are induced assuming there is one single ground truth.

there exist linguistically hard cases where there is no clear answer (Zeman, 2010;Manning, 2011), and incorporating such disagreements into the training of a model has proven helpful for POS tagging (Plank et al., 2014a;Plank et al., 2014b).


For FCM, the size of T is 1.92 × 10 7 ; potentially yielding a high variance estimator.

for LRFCM with 20-dimensional feature embeddings, the size of T is 1.28 × 10 5 , significantly smaller with lower variance.


To the best of our knowledge, our approach is novel in using the Google Books Ngram corpus for the word context, Open Thesaurus for the synonyms, and real web frequencies for disambiguating synonym candidates.

google Ngram have been previously used to find synonyms, for instance to expand user queries by including synonyms (Baker and Lamping, 2011).


As it can be observed from the frequency band results and the complexity measure, LexSiS offers better synonyms for high frequency and not for low frequency words.

our method improves with low frequency complex words.


These systems show an acceptable level of accuracy, they are easy to build and are highly computationally efficient as the only operation required to assign a polarity label are the word lookups and averaging.

the information about word polarities in a document are best exploited when using machine learning models to train a sentiment classifier.


as having the highest positive sentiment score.

an SVM model assigns higher scores to bigrams containing negative words problem, bad, worries, to outweigh their negative impact.


Although a performance drop was expected due to the big genre differences, results suggest the presence of some corpus-independent features that capture cross-linguistic influence.

they also suggest that a large portion of the features helpful for NLI are genre-dependent.


Finally, we the sense embeddings for the lemma Equations (4) and (5) show the role of the mix variables: if p ij = 0, then the sense embedding E(s ij ) is completely determined by the neighbors of the sense (that is, it is equal to the weighted centroid).

if p ij = 1, then the sense embedding becomes equal to the embedding of the lemma, F (l i ).


Such ideas have been explored in the past as subcomponents of extractive summarizers (Schiffman et al., 2002;Hong and Nenkova, 2014) or as features derived from small datasets for sentence compression (Woodsend and Lapata, 2012).

in our work we rely on large corpora and exclusively focus on the task of acquiring input independent indicators of importance.


Due to the large number of "false positives" and time constraints, we cannot impose on our Assyriologist informant the task of verifying all of the system reported names for us at the moment.

the current evaluation result reveals that the systematic lemmatization on CDLI, as discussed in Section 2, follows an extremely conservative approach.


As a result, WordNet has enabled a wide variety of NLP techniques such as Word Sense Disambiguation (Agirre et al., 2014), information retrieval (Varelas et al., 2005), semantic similarity (Pedersen et al., 2004;Bär et al., 2013), and sentiment analysis (Baccianella et al., 2010).

semantic knowledge bases such as WordNet are expensive to produce; as a result, their scope and domain are often constrained by the resources available and may omit highly-specific concepts or lemmas, as well as new terminology that emerges after their construction.


Finally, we create a feature vector in the target language that is used as input for the text classifier.

due to the translation ambiguity of a word in the source language, it is important to carefully choose the translation probability for calculating the expected frequencies of the target words.


e = restrict e = restrain e = custody p(e|f, F1) 0.33 0.10 0.57 p(e|f, F2) 0.02 0.00 0.98 Table 2: Shows the translation probabilities for the source word f = , within document F 1 (military related, class is "foreign policy") and document F 2 (terror related, class is not "foreign policy").

to most previous work, we focused on the word translation problem, rather than the domain-adaptation problem for cross-lingual text classification.


Indeed, some recent work suggests that drawing attention to framing may help mitigate framing effects (Baumer et al., 2015).


Here, different framings are used to support the same position on an issue.

framing involves an ensemble of rhetorical elements to create an "interpretive package" (Gamson and Modigliani, 1989) that functions by altering the relative salience or importance of different aspects of an issue (Chong and Druckman, 2007).


Single words such as "but," "all," or "not" could arguably be related to framing.

accounts from our pilot study participants made us less likely to believe that other single words, such as "an," "of," or "that," instantiated framing.


The features used here are informed by a combination of theoretical literature on framing, our own pilot studies, and prior work in computational linguistics.

we have little means of knowing a priori which of these features will be most important or even necessary.


Precision for all feature sets is around 34% (all statisticially significantly better than the dummy and significantly indistinguishable from one another), while human average precision is 91.5%.

the three top performing feature sets (All, Lexical, and Theoretical) all achieve recall around 70%, while average recall for the human annotators is only 49.3%.


Since these models first predicted the content (SVO triples) and then generated the sentences, the S,V,O accuracy captured the quality of the content generated by the models.

in our case the sequential LSTM directly outputs the sentence, so we extract the S,V,O from the dependency parse of the generated sentence.


BLEU is the metric that is seen more commonly in image description literature, but a more recent study (Elliott and Keller, 2014) has shown METEOR to be a better evaluation metric.

since both metrics have been shown to correlate well with human eval-Model S% V% O% HVC (Thomason et al., 2014) 86.87 38.66 22.09 FGM (Thomason et al., 2014) 88  Table 1: SVO accuracy: Binary SVO accuracy compared against any valid S,V,O triples in the ground truth descriptions.


This is likely due to the fact that previous work explicitly optimizes to identify the best subject, verb and object for a video; whereas the LSTM model is trained on objects and actions jointly in a sentence and needs to learn to interpret these in different contexts.

with regard to the generation metrics BLEU and METEOR, training based on the full sentence helps the LSTM model develop fluency and vocabulary similar to that seen in the training descriptions and allows it to outperform the template based generation.


We also showed that exploiting image description data improves performance compared to relying only on video description data.

our approach falls short in better utilizing the temporal information in videos, which is a good direction for future work.


In statistical machine translation (SMT), extending the generative noisy-channel formulation (Brown et al., 1993) as a discriminative, log-linear combination of multiple models (Och, 2003) has become the state of the art.

most of the component models are still estimated by heuristics or generative training.


The Direct Translation Model 2 introduced by Ittycheriah and Roukos (2007) is similar in that it also trains millions of features on the training data.

the weights are estimated based on a maximum entropy model and the underlying translation paradigm differs from the standard phrasebased model.


2 For rapid experimentation, the translation model is trained on the in-domain TED portion of the bilingual data, which is also used for maximum expected BLEU training.

we use a large 4-gram LM with modified Kneser-Ney smoothing (Kneser and Ney, 1995;Chen and Goodman, 1998), trained with the SRILM toolkit (Stolcke, 2002).


Previous work attacked this problem by inducing new translation rules from monolingual data with a semi-supervised algorithm.

this approach does not scale very well since it is very computationally expensive to generate new translation rules for only a few thousand sentences.


Specifically, for each unlabeled source phrase f , we learn a mapping W f ∈ R d×d based on the translations of m of f 's labeled neighbors: Compared to the global projection, we require an additional k-NN query to find the labeled neighbors for each unlabeled source phrase.

this extra computation takes only a negligible amount of time, since the number of labeled phrases on the source side is significantly smaller than the number of phrases on the target side.


Even after this simplification the running time of the SLP is vastly dominated by gathering similarity statistics and by constructing the resulting graph.

once the PMI statistics are collected and the graph is constructed, actual label propagation is very fast.


A fast retrieval of those colliding phrases can be done via a hash table.

since the projection is random, it is very likely that true neighbors in the d-dimensional space fall into different bins after projection (false negatives; e.g., p 1 and p 4 in Figure 3 (a)).


For English, we collect unigrams and bigrams from the monolingual data instead.

the English monolingual corpus is much larger than the tuning and testing sets for Arabic and Urdu.


As detailed in §4.1 below, the conventional BIO scheme used in existing supersense taggers is capable of representing most of these.

it does not allow for gappy (discontinuous) uses of an expression, such as track people down.


Until now the sentiment analysis has been primarily done in a bottom-up way, starting with the classification of lexical items, then resolving the polarity of the sentence, then using discourse information to improve the lexical classification.

lexical classifiers so far produce results that are too unreliable to become a basis of a discourse-level classification.


BayesCat induces categories, which are represented through a distribution over target concepts, and a distribution over features (i.e., individual context words).

to BCF, it does not learn types of features.


Previous work has found this framework effective for machine translation (MT), making it possible to train better translation models with less effort, particularly when annotators translate short phrases instead of full sentences.

previous methods for phrase-based active learning in MT fail to consider whether the selected units are coherent and easy for human translators to translate, and also have problems with selecting redundant phrases with similar content.


For example, there are methods to select sentences that contain phrases that are frequent in monolingual data but not in bilingual data (Eck et al., 2005), have low confidence according to the MT system , or are predicted to be poor translations by an MT quality estimation system (Ananthakrishnan et al., 2010).

while the selected sentences may contain useful phrases, they will also generally contain many already covered phrases that nonetheless cost time and money to translate.


Bloodgood and Callison-Burch (2010) demonstrate results of a simulation showing that this method required less than 80% of the data required by randomly selected sentences to obtain the same accuracy.

as mentioned in the introduction, the selected full sentences include many phrases already covered in the parallel data.


Bloodgood and Callison-Burch (2010) showed that by translating the phrases selected by this method using a crowdsourcing website, it was possible to achieve a large improvement of BLEU score, outperforming similar sentence-based methods.

as mentioned in the introduction, this method has several issues.


Second, maximal phrases and their occurrence counts can be enumerated efficiently by using enhanced suffix arrays (Kasai et al., 2001) in linear time with respect to document length, removing the need to set arbitrary limits on the length of strings such as n = 4 used in previous work.

it can be easily noticed that while in the previous example p 2 is included in p 3 , their occurrence counts are close but not equivalent, and thus both are maximal phrases.


We used the same dataset as the English-Japanese translation task and the same tools in the simulation experiment (Section 5).

for training target language models, we interpolated one trained with the base data and a second trained with collected data by using SRILM (Stolcke, 2002) because the hand-made data set was too small to train a full language model using only this data.


The confidence level of single words in the proposed method is lower than in the baseline method, likely because the baseline selected a smaller amount of single words, and those se-   Table 6: Average confidence level of manual translation corresponding to phrase length lected were less likely to be technical terms.

we can confirm that the confidence level for longer phrases in the baseline method decreases drastically, while it is stably high in our method, confirming the effectiveness of selecting syntactically coherent phrases.


We suspect that this is because this headline contains three keywords "syria", "civil", "war", and also the key date information: the model was trained partly on the Libya war timeline, and therefore many features and parameters were activated in the matrix factorization framework to give a high recommendation in this testing scenario.

when evaluating the output of the joint text and vision system, we see that this error is eliminated: the selected sentence on 2011-12-02 is "Eleven killed after weekly prayers in Syria on eve of Arab League deadline".


the seed nouns are noisier due to noise in LDA.


If a large amount of target domain data is available, training everything from scratch (scrALL) achieves a very good performance and adaptation is not necessary.

if only a limited amount of in-domain data is available, efficient adaptation is critical (DT-10% & ML-10% > scr-10%).


13 We find that conversation flow features obtain the best accuracy among all listed feature types (Flow: 63%; Flow*: 65%), performing significantly higher than a 50% random baseline (binomial test p < 0.05), and comparable to audience features (60%).

the length and BOW baselines do not perform better than chance.


SRL captures important semantic representations for actions associated with verbs, which have shown beneficial for a variety of applications such as information extraction (Emanuele et al., 2013) and question answering (Shen and Lapata, 2007).

the traditional SRL is not targeted to represent verb semantics that are grounded to the physical world so that artificial agents can truly understand the ongoing activities and (learn to) perform the specified actions.


The same situation happens to the location role as most of the locations happen near the sink when the verb is wash, and near the cutting board for verbs like cut, etc.

for the patient role, there is a large difference between our approach and baseline approaches as there is a larger variation of different types of objects that can participate in the role for a given verb.


Yet, several experiments indicated that perceptual properties of concepts, such as concreteness and imageability, are important features for metaphor identification (Turney et al., 2011;Neuman et al., 2013;Gandy et al., 2013;Strzalkowski et al., 2013;Tsvetkov et al., 2014).

all of these methods used manually-annotated linguistic resources to determine these properties (such as the MRC concreteness database (Wilson, 1988)).


This suggests that linguistic word embeddings already successfully capture domain and compositional information necessary for metaphor identification.

the visual PHRASECOS1 model, when applied in isolation, tends to outperform the visual WORDCOS model.


We thus expect that using video data along with the images as input to the acquisition of visual embeddings is likely to improve metaphor identification performance for verbal metaphors.

we leave the investigation of this issue for future work.


The performance of our method on the same dataset is a little lower than that of Tsvetkov et al.

we do not use any hand-annotated resources and acquire linguistic, domain and perceptual information in the data-driven way.


Some notable neural network based approaches here include the works of (Klementiev et al., 2012;Zou et al., 2013;Mikolov et al., 2013;Hermann and Blunsom, 2014b;Hermann and Blunsom, 2014a;Chandar et al., 2014;Soyer et al., 2015;Gouws et al., 2015).

except for (Hermann and Blunsom, 2014a; Hermann and Blunsom, 2014b), none of these other works handle the case when parallel data is not available between all languages.


None of these captions actually correspond to the image as per our parallel image caption test set.

clearly the first, third and fourth caption are semantically very relevant to this image as all of them talk about baseball.


We also observed that adding visual features to textual features improves performance in some cases: multimodal features perform better than textual features alone both for object labels (CNN+O) and for image descriptions (CNN+C).

adding CNN features to textual features based on object labels and descriptions together (CNN+O+C) resulted in a small decrease in performance.


Common sense knowledge has been predominately created directly from human input or extracted from text (Lenat et al., 1990;Liu and Singh, 2004;Carlson et al., 2010).

our work is focused on visual common sense extracted from images anno- The angle between the centroid of a and the centroid of b lies between 315° and 45°, or 135° and 225°.


It may be possible to write GPU-specific code that maintains the entire parse state on GPU, but we are not aware of any such implementations.

our supertagger only uses matrix operations, and does not take any parse state as inputmeaning it is straightforward to run on a GPU.


These models are 0.3 and 1.5 F1 more accurate than the C&C baseline respectively, which is well within the margin of improvement obtained by our model.

to standard parsing algorithms, the efficiency of our model depends directly on the accuracy of the supertagger in guiding the search.


Recently, bi-LSTMs have achieved high accuracies in a simpler sequence labeling task: partof-speech tagging (Wang et al., 2015;Ling et al., 2015) on the Penn treebank, with small improvements over local models.

we achieve strong accuracies compared to (Wang et al., 2015) using feed-forward neural network model trained on local context, showing that this task does not require bi-LSTMs.


It uses word segmentation first and feeds the segmented words to subsequent task(s), named as Word Unit.

this method suffers from the error propagation problem since an incorrect word segmentation would cause an error in the subsequent task.


If an error results in a word crossing the boundary of semantic slots, it will definitely lead to an error in SLU semantic slot filling.

when supplying the automatic 'BIES' ngrams from CWS to SLU semantic slot filling (As Features), we observe a nice gain in both cases, 94.41% for CTB6 and 94.13% for PKU.


For the sentence, the baseline system extracts '湖南财' as a location name.

the word segmentation separates the words '湖 南' (Hunan) and '财政' (Finance), which reduces the probability score of '湖南财' being a slot value because it crosses word boundaries.


When CWS accuracy is high on the training data, the NER model trained with such data puts more weight on word segmentation features rather than character features.

during testing, the performance of CWS drops, resulting in more word segmentation errors, with a high chance to propagate to NER errors; even worse, a lot of these CWS errors are around NERs since a lot of NERs are OOVs and thus are challenging to segment correctly.


However, most of previous work relied on substantial amount of resources such as language-specific rules, basic tools such as part-of-speech taggers, a large amount of labeled data, or a huge amount of Web ngram data, which are usually unavailable for low-resource ILs.

in this paper we put the name tagging task in a new emergent setting where we need to process a surprise IL within very short time using very few resources.


Coupled with embedding approaches (Mikolov et al., 2013;Le and Mikolov, 2014;Pennington et al., 2014), neural networks can find the optimal feature combinations using techniques such as random weight initialization and back-propagation, and have established the new state-of-the-art for several tasks (Socher et al., 2013;Devlin et al., 2014;Yu et al., 2014).

neural networks are not as good at optimizing combinations between sparse features, which are still the most dominating factors in natural language processing.


Each high dimensional feature in F is induced for making classification between two labels, y andŷ, but it may or may not be helpful for distinguishing labels other than those two.

our algorithm can be modified to learn the weights of the induced features only for their relevant labels by adding the label information to F, which would change the line 13 in Algorithm 1 as follows: introducing features targeting specific label pairs potentially confuses the classifier, especially when they are trained with the low dimensional features targeting all labels.


One could collect large gazetteers from knowledge graph and phrase embeddings to obtain high coverage of gazetteers.

large gazetteers cause a side-effect called "feature under-training", where the gazetteer features overwhelm the context features.


So far we have described a model for learning structures for a single event.

the inference of the event types for individual events may depend on other events that are mentioned in the document.


As pipeline approaches suffer from error propagation, researchers have proposed methods for joint extraction of event triggers and arguments, using either structured perceptron (Li et al., 2013), Markov Logic (Poon and Vanderwende, 2010), or dependency parsing algorithms (McClosky et al., 2011).

existing joint models largely rely on heuristic search to aggressively shrink the search space.


Our work is similar to Riedel and McCallum (2011).

there are two main differences: first, our model extracts both event mentions and entity mentions; second, it performs joint inference across sentence boundaries.


In the pipelined approach, it is often simple for the argument classifiers to realize that camera-man is the Target argument of the Die event due to the proximity between cameraman and died in the sentence.

as cameraman is far away from fired, the argument classifiers in the pipelined approach might fail to recognize cameraman as the Target argument for the event Attack with their local features.


Recurrent Neural Networks (RNNs) have shown impressive performances on many sequential modeling tasks due to their ability to encode unbounded input histories.

training simple RNNs is difficult because of the vanishing and exploding gradient problems (Bengio et al., 1994;Pascanu et al., 2013).


Again, in many cases attention weights concentrate around the last word (bottom row).

we observe that many long distance words also receive noticeable attention mass.


Because they are discriminatively trained, these meth-ods can learn representations that yield very accurate predictive models (e.g., .

in comparison with the probabilistic graphical models that were previously the dominant machine learning approach for NLP, neural architectures lack flexibility.


• If the model is trained to maximize the joint likelihood of the discourse relations and the text, it is possible to marginalize over discourse relations at test time, outperforming language models that do not account for discourse structure.

to recent work on continuous latent variables in recurrent neural networks (Chung et al., 2015), which require complex variational autoencoders to represent uncertainty over the latent variables, our model is simple to implement and train, requiring only minimal modifications to existing recurrent neural network architectures that are implemented in commonly-used toolkits such as Theano, Torch, and CNN.


Also, these prior models employ continuous latent variables, requiring complex inference techniques such as variational autoencoders (Kingma and Welling, 2014;Burda et al., 2016;Chung et al., 2015).

the discrete latent variables in our model are easy to sum and maximize over.


They can vary in terms of the number of words that contain a given phonestheme, their frequencies, the strength of their association with the core meaning of a phonestheme (for example measured as an average of human ratings for all the words that comprise the given phonesthemic cluster) and the regularity of that association (what proportion of words in the whole cluster are highly related to the predicted meaning).

psycholinguistic data is to some extent ambiguous on how these features of phonesthemes affect their productivity, learnability and their effect on language processing, which could partly be due the methods being employed.


Both studies found support for a sizable proportion of phonetic units tested.

it could still be questioned whether the comparison was sufficiently strict, given that sets of random words which do not overlap in form have a priori lower chance of being semantically related than sets of words that share a phonestheme.


The fact that we obtain significant results indicates that our generated labels are meaningful not only according to automatic evaluation measures but also in terms of what speakers can perceive.

the pattern of which phonesthemic labels receive better human judgments is somewhat less clear.


One possible way to identify this relevant nugget is to apply sentence compression techniques (McDonald, 2006;Siddharthan, 2011;Štajner et al., 2013;Filippova et al., 2015;Filippova and Strube, 2008;Narayan and Gardent, 2014;Cohn and Lapata, 2009).

all these methods have been developed for standard texts with complete sentences, and it is not clear whether they are suited to dictionary definitions.


For example, (Xie et al., 2013) has proposed to use semantic frame parsers to generalize from sentences to scenarios to detect the roles of specific companies (positive or negative), where support vector machines with tree kernels are used as predictive models.

(Ding et al., 2014) has proposed to use various lexical and syntactic constraints to extract event features for stock forecasting, where they have investigated both linear classifiers and deep neural networks as predictive models.


NMTbased systems thus may help ameliorate the lack of large error-annotated learner corpora for GEC.

nMT models typically limit vocabulary size on both source and target sides due to the complexity of training (Sutskever et al., 2014;Luong et al., 2015;Jean et al., 2015).


Our work is also related to research on reference resolution in dialogue systems, such as Kennington and Schlangen (2015).

unlike Kennington and Schlangen, who explicitly train an object recognizer associated with each word of interest, with at least 65 labeled positive training examples per word, our model does not have any comparable form of supervision and our data exhibits much lower frequencies of object and word (co-)occurrence.


To use maximum likelihood or minimum cross-entropy, it assumes that the model distribution is peaked.

especially in natural language processing where the ambiguity is ubiquitous, this assumption does not hold.


Such work typically trains semi-supervised classifiers to determine events of interest due to the limitation of annotated data.

a few studies devote to open domain event extraction (Benson et al., 2011;Ritter et al., 2012;Petrović et al., 2010;Diao et al., 2012;Chierichetti et al., 2014;Li et al., 2014;Qiu and Zhang, 2014), in which an event category is not predefined, and clustering models are applied to automatically induce event types.


2015, using a set of seed events and large raw tweets for ER.

we take a fully-automated approach to find seed events, since manual listing of seed DDoS events can be a costly and time consuming process, and requires a certain level of expert knowledge.


From the curves we can see that the sparse representation is comparatively less efficient in picking out negative examples, since at a lower recall the model does not gain a higher precision.

lSTM-based representation demonstrates a better trade-off between recall and precision.


They used the techniques for traditional SMT models, under the IBM framework (Watanabe and Sumita, 2002) or the feature-driven linear models (Finch and Sumita, 2009;Zhang et al., 2013).

the target-bidirectional techniques we have developed for the unified neural network framework, target a pressing need directly motivated by a fundamental issue suffered by recurrent neural networks.


The remaining 14 games end before they should because the judge bot breaks down.

the players do reveal their own roles after the game ends.


We also considered the number of verbs, based on our intuition that heavy use of verbs can be associated to motion.

we don't expect the number of motion words to be as important in our domain.


A measure of language complexity is the type-token ratio (TTR).

hesitations (um, er, uh), hedges (sort of, kind of, almost), and polite forms are markers of powerless language (Sparks and Areni, 2008).


We did not consider word sequence, player interaction, individual characteristics of players, or non-literal meaning.

the data set is too small for any more complex models.


The most extensively developed resource for English is the MRC Psycholinguistic Database (Section 2).

it is far from complete, most likely due to the inherent cost of manually entering such properties.


Meanwhile, some improved linear analysis methods were proposed for encoding documents with a reliable similarity information (Yih et al., 2011;Chang et al., 2013).

all those works for document representation paid little attention to the variability of intra-topic documents.


For the generative story of our new model, we again begin by generating pairs ( i , c i ).

instead of typesetting c i , we generate a distinct glyph character g i as its replacement, according to Orthographic substitution patterns are language-specific, and thus P GLYPH is as well.


As a second baseline, we compare to our previous work, which improved Ocular's diplomatic transcription accuracy by introducing orthographic variation directly into the LM with hand-constructed language-specific orthographic rules to rewrite the LM training data prior to n-gram estimation (Garrette et al., 2015).

this rule-based preprocessing approach is inadequate in many ways.


Traditionally, bag of words representation of surrounding context has shown reasonably good performance.

the information contained in the bag of words vector is very sensitive to context window size.


Usually a way to tackle this problem is to try different context window sizes and use the one that gives the highest validation performance.

this method cannot be easily applied to our task, because different medical events like medication, diagnosis or adverse drug reaction require different context window sizes.


In this sentence, the true labels are Adverse Drug Event(ADE) for "bronchiolitis obliterans" and Drugname for "ABVD chemo".

the ADE , "bronchiolitis obliterans" could be misslabeled as just another disease or symptom, if the entire sentence is not taken into context.


It is possible that with a larger dataset, LSTM might perform comparable to or better than GRU.

our experiments with reducing the hidden layer size of LSTM-document model to control for the number of trainable parameters did not produce any significant improvements.


Moreover, figure 4 seems to indicate that there is not much difference between the performances of LSTM and GRU with different data sizes.

it is clearly surprising that RNN models with a larger number of parameters can still perform better than CRF models on smaller dataset sizes.


RNNs are excellent in extracting relevant patterns from sequence data.

they do not explicitly enforce constraints or dependencies over the output labels.


Given the bag of words {John, loves, Mary}, the action SHIFT-John-NNP is still different from the action SHIFT-Mary-NNP.

the action component of the features becomes SHIFT only, and the words John/ Mary must be used as lookahead configuration features for their disambiguation.


State-of-the-art word embeddings, which are often trained on bag-of-words (BOW) contexts, provide a high quality representation of aspects of the semantics of nouns.

their quality decreases substantially for the task of verb similarity prediction.


Nonetheless, the human judgment scores in these datasets reflect relatedness between words.

the recent SimLex999 dataset (Hill et al., 2014) contains word similarity scores for nouns (666 pairs), verbs (222 pairs) and adjectives (111 pairs).


Though with the help of GPU, BLSTM-RNN is still slower than the other methods.

it should be noted that the speed of our approach is acceptable compared with previous neural network language model based methods, including (Bengio et al., 2003;Mikolov et al., 2010;Mnih and Hinton, 2007), as our model uses a much simpler output layer which only has two nodes, avoiding the time consuming computation of the big softmax output layer in language model.


We achieve an F1 score of 73.4 with them.

the difference to using position embeddings with entity flags is not statistically significant.


Multitask learning has also been used for other classification and regression tasks in language processing, mostly for domain adaptation (Daume III, 2007;Finkel and Manning, 2009), but also more recently for tasks such as multi-emotion analysis (Beck et al., 2014), where the each emotion explaining a text is defined as a task.

in all previous work the focus has been on addressing task variance coupled with data scarcity, which makes them different from the work we describe in this paper.


Previous work in the area have proposed a number of methods for identifying and extracting task knowledge from search query sessions (Mehrotra and Yilmaz, 2015b;Wang et al., 2013;Lucchese et al., 2011;Verma and Yilmaz, 2014;Mehrotra and Yilmaz, 2015a).

while some tasks are fairly trivial and single-shot (e.g.


(Jones and Klinkner, 2008) was the first work to consider the notion that there may be multiple sub-tasks associated with a user's informational needs.

they fall short of proposing a method to identify a task from queries.


The probability of a question given a label under this model is: Theoretically, we could learn both of the production rule distributions that compose P (w, t|L; θ) in this formulation.

in practice, the large number of nonterminals makes it challenging to learn a conditional probability table for the binary production rules.


phenomenon found that reducing the length of the templates made it even more difficult for these models to find correct parses for long questions.

to these baselines, our models do not suffer from either of these problems because the logical form derivation grammar restricts the search to correct derivations and our generative objective prefers frequently-occurring lexicon entries.


ASR (Adda-Decker and Adda, 2000), MT (Koehn and Knight, 2003) or IR (Monz and de Rijke, 2001), and is generally perceived as a crucial component for the processing of respective languages.

most existing systems rely on dictionaries or are trained in a supervised fashion.


The suffix-prefix-based approach results to Bundes-finanz-ministerium and the prefixsuffix method to Bund-esfinanz-ministerium.

for some words, the prefix-suffix generates the correct compound split, e.g.


using the similar candidate units first and only apply the other candidate sets if no split was found.

preliminary experiments revealed that it was always beneficial to generate splits based on all three candidate sets and use the geometric mean scoring as outlined above to select the best split as decomposition of a word.


This is caused by many short split candidates that are not detected due to the ml parameter.

our method still beats the KK baseline significantly.


Note that Dijkstra's algorithm is exact; no beam search is required as in some neural sequence models.

,ŷ may not be the most probable string-extracting that from a weighted FST is NP-hard (Casacuberta and de la Higuera, 1999).


As an alternative, machine learning models have been proposed to generate inflections from root forms as string transduction (Yarowsky and Wicentowski, 2000;Wicentowski, 2004;Dreyer and Eisner, 2011;Durrett and DeNero, 2013;Ahlberg et al., 2014;Hulden, 2014;Ahlberg et al., 2015;Nicolai et al., 2015).

these impose either assumptions about the set of possible morphological processes inflection generation kalb kälber case=nominative number=plural (e.g.


Our model predicts the sequence of characters in the inflected string given the characters in the root word (input).

our problem differs from the above setting in two ways: (1) the input and output character sequences are mostly similar except for the inflections; (2) the input and output character sequences have different semantics.


In an error analysis, it turned out that FF2010 (i.e., SMOR) cannot process 2% of the gold samples.

on a common processable test set, we still find our system to outperform FF2010 significantly, which indicates that the difference in performance is not just a matter of coverage.


Pun generation is much more complicated than target recovery as reflected in the complexity of proposed systems for humor generation.

improved understanding of puns by way of progress in the target recovery task should also lead to corresponding improvements in the task of pun generation.


For example, perturbed and perturbs are inflections of the verb perturb.

derivation modifies words more drastically-often changing the meaning or POS.


The standard measure for the supervised task is border F 1 , which measures how often the segmentation boundaries posited by the model are correct.

this measure assumes that the concatenation of the segments is identical to the input string (i.e., surface segmentation) and is thus not applicable to canonical segmentation.


However, this measure assumes that the concatenation of the segments is identical to the input string (i.e., surface segmentation) and is thus not applicable to canonical segmentation.

the Morpho Challenge competition (Kurimo et al., 2010) uses a measure that samples a large number of word pairs from a linguistic gold standard.


BP is guaranteed to converge to an exact solution if the graph is a tree.

exact inference is intractable for general graphs, i.e., graphs with loops.


Then they trained a linear-CRF model on additional in-domain data, reducing the error up to 22%.


Previous work has made significant progress on this task (Chen and Mooney, 2008;Angeli et al., 2010;Konstas and Lapata, 2012).

most approaches solve the two content selection and surface realization subtasks separately, use manual domain-dependent resources (e.g., semantic parsers) and features, or employ template-based generation.


They employ a probabilistic context-free grammar that specifies the structure of the event records, and then treat generation as finding the best derivation tree according to this grammar.

their method still selects and orders records in a local fashion via a Markovized chaining of records.


Recently the connection between less formulaic language and simple actions has been explored successfully in the context of simulated worlds (Branavan et al., 2009;Goldwasser and Roth, 2011;Branavan et al., 2011;Artzi and Zettlemoyer, 2013;Andreas and Klein, 2015) and videos (Malmaud et al., 2015;Venugopalan et al., 2015).

to our knowledge, there is no body of work that focuses on understanding the relation between natural language and complex actions and goals or on explaining flexibly the actions taken by a robot in natural language utterances.


When the blocks are marked with clearly identifiable logos, all models outperform our baselines by a wide margin.

when blocks are blank the situation is flipped.


Our results show that including inter-document contextual information yields additional improvements to those obtained from inter-sentence information.

as expected, the former are smaller than the latter, as sentences in the same post are more related than sentences in different posts.


In this paper, we assume that these properties do not correlate and therefore the ultradense subspaces do not overlap, e.g., D s ∩ D c = ∅.

this might not be true for other settings, e.g., sentiment and semantic information.


If you are thinking of buying a TV for watching football, you might go to websites such as Amazon to read customer reviews on TV products.

there are many products and each of them may have hundreds of reviews.


In the first step, features are extracted based on association rules or dependency patterns, and in the second step features are grouped into aspects using clustering algorithms.

our method extracts features and groups them at the same time.


In Naive Bayes (NB) we learn the verb-torelation mapping weights from labeled training instances.

to the other systems, EM allows learning from both labeled and unlabeled instances.


They also compute heuristic confidences in verb-to-relation mappings from label propagation scores, which are not probabilities.

we map verbs directly to relations, and obtain P (v p |r i ) as an integral part of our EM process.


Structured knowledge about the world is useful for many natural language processing (NLP) tasks, such as disambiguation, question answering or semantic search.

the extraction of structured information from natural language text is challenging because one relation can be expressed in many different ways.


While it uses the whole context for convolution, it performs max pooling over the three parts individually.

we propose to split the context even earlier and apply the convolutional filters to each part separately.


The number of positive examples per slot and year ranges from 0 (org:member of, 2014) to 581 (per:title, 2013), the number of negative examples from 5 (org:website, 2014) to 1886 (per:title, 2013).

to other relation classification benchmarks, this dataset is not based on a knowledge base (such as Freebase) and unrelated text (such as web documents) but directly on the SF assessments.


1 Factorization (2) relies on the hypothesis that there exists a fixed vector for each candidate answer representing its meaning.

as we argued in Section 1, an entity surface does not possess meaning; rather, it serves as an anchor to link pieces of information about it.


In contrast, significant degradation in BLEU is observed at 5000wpm for Chinese-to-English, a language pair with complicated reordering requirements -notice that all methods consistently keep a very high distortion limit for this language pair.

both BO-S and BO-D strategies yield better performance on test (at least +0.5BLEU improvement) than the grid and random search baselines.


The approach offers impressive coverage, avoids issues of distant supervision, and provides a useful exploratory tool.

openIE predictions are difficult to use in downstream tasks that expect information from a fixed schema.


competitions use distant supervision (Ji and Grishman, 2011).

distant supervision provides noisy training data with many false positives, and this limits the precision of the resulting extractors (see Section 2).


They added up to 20K instances of crowd data to 1.8M DS instances using sparse logistic regression, tuning the relative weight of crowdsourced and DS training.

they saw only a marginal improvement from F1 0.20 to 0.22 when adding crowdsourced training to DS training, and conclude that human feedback has little impact.


As a result, it can process various interactions between entity vectors.

it is difficult to process large-scale knowledge graphs with NTN due to its high complexity.


That is, when a triple (h, r, t) is given, h and t plays different roles.

the existing embeddings treat them equally and embed them into a space in the same way.


A taskspecific model ( Figure 1) that only learns from indomain annotations supports only (2).

a non-hierarchical joint model ( Figure 4) supports only (1): it learns a single shared w applied to any test pair regardless of task or domain.


As in the DA experiments, we compute average performance over twenty random train/test splits for each training set size.

figure 6 shows STS results for all models across the individual model does better than in DA: it overtakes the global model with fewer training examples and the differences with the adaptive model are smaller.


Is it really effective to "cram" whole sentence meanings into fixed-length vectors?

we focus on capturing fine-grained word-level information directly.


Our pairwise word interaction model shares similarities with recent popular neural attention models (Bahdanau et al., 2014;Rush et al., 2015).

there are important differences: For example, we do not use attention weight vectors or weighted On the Mat There Sit Cats Cats Sit On the Mat Figure 3: The similarity focus layer helps identify important pairwise word interactions (in black dots) depending on their importance for similarity measurement.


In most bitexts, source and target sentences have roughly the same length.

for our task of aligning text and speech where the speech is represented as a sequence of phones or PLP vectors, the source can easily be several times larger than the target.


In Table 4 and 5, we can see that though we add a strong hierarchical phrase-based reordering model in the baseline, our model can still bring a maximum gain of 0.59 BLEU score, which suggest that our model is applicable and robust in various circumstances.

we have noticed that the gains in Arabic-English system is relatively greater than that in Chinese-English system.


Most works either apply manual annotation (Yang et al., 2015) or use existing but small-scale resources such as the Penn Treebank (Chung and Gildea, 2010;Xiang et al., 2013).

we employ an unsupervised approach to automatically build a largescale training corpus for DP generation using alignment information from parallel corpora.


This makes the input sentences and DP-inserted TM more consistent in terms of recalling DPs.

the above method suffers from a major drawback: it only uses the 1-best prediction result for decoding, which potentially introduces translation mistakes due to the propagation of prediction errors.


(2012) propose both simple rule-based and manual methods to add zero pronouns in the source side for Japanese-English translation.

the BLEU scores of both systems are nearly identical, which indicates that only considering the source side and forcing the insertion of pronouns may be less principled than tackling the problem head on by integrating them into the SMT system itself.


In Case A, "Do you" in the translation output is compensated by adding DP 你 (you) in (b), which gives a better translation than in (a).

in case C, our DP generator regards the simple sentence as a compound sentence and insert a wrong pronoun 我 (I) in (b), which causes an incorrect translation output (worse than (a)).


In (b), when integrating an incorrect 1-best DP into MT, we obtain the wrong translation.

in (c), when considering more DPs (2-/4-/6-best), the SMT system generates a perfect translation by weighting the DP candidates during decoding.


As with most previous work on coreference resolution, we only consider mentions that are noun phrases.

not all of the noun phrases are mentions.


If a coreferent mention is classified as non-coreferent, the recall of the coreference resolver that uses the singleton detector will decrease.

recall errors only affect the singleton detector itself and not coreference resolvers.


For example, Hobbs' algorithm and agreement features are being used successfully in the Stanford system (Lee et al., 2013).

apart from features like these, a large number of linguistically motivated features have been proposed which either do not have a significant impact or are only applicable to a specific language or domain.


(2015) are incorporated in the Stanford system in a heuristic way: if both anaphor and antecedent are classified as singleton, and none of them is a named entity, then those mentions will be disregarded.

since our Confident model does have a high precision, we use it for removing all non-coreferent mentions in a preprocessing step.


In recent years, several supervised entity coreference resolution systems have been proposed, which, according to Ng (2010), can be categorized into three classes -mention-pair models (McCarthy and Lehnert, 1995), entity-mention models (Yang et al., 2008a;Haghighi and Klein, 2010;Lee et al., 2011) and ranking models (Yang et al., 2008b;Durrett and Klein, 2013;Fernandes et al., 2014) -among which ranking models recently obtained state-of-the-art performance.

the manually annotated corpora that these systems rely on are highly expensive to create, in particular when we want to build data for resource-poor languages (Ma and Xia, 2014).


Relation Extraction All LRFR-TUCKER models improve over BASELINE and FCM (Table 3), making these the best reported numbers for this task.

lRFR-CP does not work as well on the features with only one lexical part.


Summary For unigram lexical features, LRFR n -TUCKER achieves better results than LRFR n -CP.

in settings with fewer training examples, features with more lexical parts (n-grams), or when faster predictions are advantageous, LRFR n -CP does best as it has fewer parameters to estimate.


Since the size of English vocabulary W may be up to 10 6 scale, hierarchical softmax and negative sampling (Mikolov et al., 2013b) are applied during training to learn the model efficiently.

using CBOW to learn Chinese word embeddings directly may have some limitations.


This phrase in turn combines with other contextual categories using CCG combinators to form new categories representing larger phrases.

to phrase structure trees, CCG derivation trees encode a richer notion of syntactic type and constituency.


Automated geolocation of social media messages can benefit a variety of downstream applications.

these geolocation systems are typically evaluated without attention to how changes in time impact geolocation.


Notably, Monday is significantly harder, with an accuracy of 1.5 standard deviations below the mean.

the hour of the day has much more significant impact on accuracy; some times of the day are significantly easier and harder than the average.


Major progress has been made in this task in recent years, due primarily to the SemEval Semantic Textual Similarity (STS) task (Agirre et al., 2012;Agirre et al., 2013;Agirre et al., 2014;Agirre et al., 2015).

the utility of top STS systems has remained largely unexplored in the context of short answer grading.


For three of the language pairs we observed increases in BLEU scores over the baseline for all interlocking methods with substantial gains of 1.9 to 2.6 BLEU points coming from the source interlocking technique.

the German to English pair gave a negative result.


In the past, the prevalent criterion was to judge the quality of a translation in terms of fluency and adequacy, on an absolute scale (White et al., 1994).

different evaluators focused on different aspects of the translations, which increased the subjectivity of their judgments.


2015, we only consider a monolingual evaluation scenario and ignore the source text .

our features and experimental setup can be extended to include source-side features.


In the dependency parsing framework, some previous work incorporated MWE annotations within syntactic trees, in the form of complex subtrees either with flat structures (Nivre and Nilsson, 2004;Eryigit et al., 2011;Seddah et al., 2013) or deeper ones Candito and Constant, 2014).

these representations do not capture deep lexical analyses like nested MWEs.


This framework looks appealing in order to test our assumption that segmentation and parsing are mutually informative, while leaving the exact flow of information to be learned by the system itself: we do not postulate any priority between the tasks nor that all attachment decisions must be taken jointly.

we expect most decisions to be made independently except for some difficult cases that need both lexical and syntactic knowledge.


Word-sentiment associations are commonly captured in sentiment lexicons.

most existing manually created sentiment lexicons include only single words.


Previous work on Automatic Paraphrase Identification (PI) is mainly based on modeling text similarity between two sentences.

we study methods for automatically detecting whether a text fragment only appearing in a sentence of the evaluated sentence pair is important or ancillary information with respect to the paraphrase identification task.


This uses an alignment approach based on lexical similarity, which may fail to align some text constituents.

these mistakes only affect the precision in extracting ATFs rather than the recall.


The condition over d is important because the sentence aligner may fail to match some subsequences, creating false ATFs.

what is missed from one sentence will be missed also in the other sen-Train Test τ Ancillary Important Total Ancillary Important Total  1  971  687  1658  387  687  1074  2  426  364  790  166  151  317  3  166  169  335  62  79  141  4  59  73  132  21 36 57 .


SMT systems generate scored candidates and select a sentence having the highest score as the translation result.

the 1-best result of SMT system is not always the best result because the scoring is conducted only with local features.


(R. Riggs) We have analyzed stylistic patterns in quotations.

are these patterns characteristic of quotations?


Our work is also inspired by the recent work in introducing datasets to evaluate question answering and reading comprehension tasks that require reasoning and entailment.


Desegmentation is usually applied to the 1-best output of the decoder.

this pipeline suffers from error propagation: errors made during decoding cannot be corrected, even when desegmentation results in an illegal or extremely unlikely word.


Lattice rescoring also involves many steps, requiring one to train and tune a complete segmented system with segmented references, then desegment lattices and compose them with a word LM, and then tune a lattice rescorer on unsegmented references.

our system is implemented as a single decoder feature function in Moses.


As we will see in Section 9, there is a wealth of existing methods for learning representations that capture context of words in two different languages in the literature.

they have been evaluated on tasks that do not require much semantic analysis, such as translation lexicon induction or document categorization.


This is an instance of sparse coding, which consists of modeling data vectors as sparse linear combinations of basis elements.

with dimensionality reduction techniques such as PCA, the learned basis vectors need not be orthogonal, which gives more flexibility to represent the data (Mairal et al., 2009).


Equations 3 and 4 define non-differentiable, nonconvex optimization problems and finding the globally optimally solution is not feasible.

various methods used to solve convex problems work well in practice.


Cosine similarity is unable to differentiate between these two cases, assigning a high score to both these pairs, causing both of them to be labeled positive.

balAPinc with sparse representations teases them apart by giving a high score to the first pair and a low score to the second.


The normalization constant is Z(u) = w∈V p(w | u), and V is the vocabulary.

the standard approach for estimating neural language models is maximum liklelihood estimation (MLE), where we learn the parameters θ * that maximize the likelihood of the training data, for each training instance, gradient-based approaches for MLE require a summation over all units in the output layer, one for each word in V .


We generate candidate properties from the vehicle and event words of a simile.

when the event is a form of "to be" or a perception verb (taste, smell, feel, sound, look), we do not generate candidate properties from the event because the verb is too general.


Given the nature of the complex storytelling task, the best and most reliable evaluation for assessing the quality of generated stories is human judgment.

automatic evaluation metrics are useful to quickly benchmark progress.


We find that using a beam size of 1 (greedy search) significantly increases the story quality, resulting in a 4.6 gain in METEOR score.

the same effect is not seen for caption generation, with the greedy caption model obtaining worse quality than the beam search model.


They demonstrated that asking human to translate these DTPs can bring a significant gain to the overall translation quality compared to translating other phrases.

to our Source 南亚 各国 外长 商讨 自由 贸易区 和 反 恐 问题 (south asian)(countries)(foreign minister)(discuss) (free)(trade zone)(and)(anti)(terrorism)(issue) Ref south asian foreign ministers discuss free trade zone and anti-terrorism issues Baseline south asian foreign ministers to discuss the issue of free trade area and the L2R south asian foreign ministers discuss the issue of free trade area and the PR south asian foreign ministers discuss free trade area and anti-terrorism issues Table 1: Examples of applying the Left-to-right (L2R) framework and the Pick-Revise framework (PR) in modifying a Chinese-English translation.


But the uses only need to perform one type of actions, which might be more suitable to be performed by a single human translator.

the improvement is relatively small compared to fully simulated results, suggesting that human involvement is still critical for improve the translation quality.


In this case every translator will focus on a single action, which might be easier to train and may have higher efficiency.

the performance of current framework is still related to the underlying MT system.


These represent two extremes of the system: consuming the maximum amount of context, which might give the most robust representation of topic semantics, and consuming the minimum amount of context, which gives the most focused representation of topics semantics (and which more generally might allow the system to directly memorize train-test pairs observed in training).

neither performs as well as the combination of all CNN features, showing that the different granularities capture complementary aspects of the entity linking task.


This way CNNs can efficiently learn to embed input sentences into low-dimensional vector space, preserving important syntactic and semantic aspects of the input sentence.

engineering features spanning two pieces of text such as in QA is a more complex task than classifying single sentences.


Although the above approaches are very valuable, they required considerable effort to study, define and implement features that could capture relational representations.

we are interested in techniques that try to automatize the feature engineering step.


(2015) 81  V AE+QE , again align with the state of the art.

our CTK using CH largely outperforms all previous work, e.g., 7.6 points more than CNN R in terms of MRR.


Our preliminary experiments using word2vec were not successful.

cNNs may provide a more effective similarity.


Such models can be used to effectively summarize occurrences of patterns in text and aggregate them into a vector representation.

the summary produced is not selective since all pattern occurrences are counted, weighted by how cohesive (non-consecutive) they are.


's (2011) convolutional model (discussed above) gives 81.47% F1 on the English test set when trained on only the gold data.

by using carefully selected word-embeddings trained on external data, they are able to increase F1 to 88.67%.


In order to transfer annotations, we align monolingual embeddings between languages.

a full fine-grained alignment is not possible with only ten translation pairs due to differences between the languages and variations across raw corpora from which the embeddings are derived.


Muralidharan and Hearst, 2013;Pettersson and Nivre, 2011).

these texts differ from contemporary training corpora in a number of linguistic respects, including the lexicon (Giusti et al., 2007), morphology (Borin and Forsberg, 2008), and syntax (Eumeridou et al., 2004).


(2007) report an increase of about 3% accuracy on adaptation of POS tagging from Modern English texts to Early Modern English texts if the target texts were automatically normalized by the VARD system.

normalization is not always a well-defined problem (Eisenstein, 2013), and it does not address the full range of linguistic changes over time, such as unknown words, morphological differences, and changes in the meanings of words (Kulkarni et al., 2015).


We did not follow their setting because it would lead to a significant change of test data.

it should be noted that these "errors" are not particularly meaningful for linguistic analysis, and could easily be addressed by heuristic post-processing.


For this reason, they have the potential of uncovering external relations involving language isolates and tiny language families such as Ainu, Basque, and Japanese.

our understanding of typological changes is far from satisfactory in at least two respects.


A central problem of the tree model is its assumption that after a branching event, two resultant languages evolve completely independently.

linguists have noted that horizontal contact is a constitutive part of evolutionary history.


For reasons unknown to us, they chose clustering models that basically assume tree-like evolution (Saitou and Nei, 1987;Bryant and Moulton, 2004).

creole genesis is more comparable to models that explicitly take into account genetic admixture (i.e., contact phenomena).


Combined with the PCA analysis in Section 4.2, this suggests that Japanese is a very non-creole-like language.

we are unsure if the possibility of creole status for (pre-)Old Japanese is completely rejected.


more irrelevant words are included in the window.

the negative effect on the accuracy is still relatively small, up to around −0.1 for the models using French and Russian as the second languages, and −0.25 for Czech when setting m = ∞.


This is unsurprising considering the historical importance of count-based models in which every surface form of a word is a separately modeled entity (English cat and Spanish gato would not likely benefit from sharing counts).

recent models that use distributed representations-in particular models that share representations across languages (Hermann and Blunsom, 2014;Faruqui and Dyer, 2014;Huang et al., 2015;Lu et al., 2015, inter alia)-suggest universal models applicable to multiple languages are a possibility.


Interpolation of monolingual LMs is an alternative to obtain a multilingual model (Harbeck et al., 1997;Weng et al., 1997).

interpolated models still require a trained model per language, and do not allow parameter sharing at training time.


One possibility is that order-critical sentences that cannot be disambiguated by a robust conceptual semantics (that could be encoded in distributed lexical representations) are in fact relatively rare.

it is also plausible that current available evaluations do not adequately reflect order-dependent aspects of meaning (see below).


Finally, we observe that the n-grams feature type turns out to be the most domain-dependent in our evaluation.

both the syntax and the part of speech features appear quite robust across domains.


Related research has already used the argumentation framework of Dung (1995) to find accepted arguments based on such a graph on a much smaller scale (Cabrio and Villata, 2012a).

the size of the web would allow for recursive analysis of the graph with statistical approaches like the famous PageRank algorithm (Page et al., 1999), enabling an assessment of argument relevance.


Using larger subgraphs yields higher accuracy, because they capture more structural information.


We assigned 20% of instances to the test split, totalling 378 instances (Section 6).

some of these instances cannot be obtained using predicted role labels: a missing or incorrect semantic role will unequivocally lead to positive interpretations that are not in our corpus and thus evaluation is not straightforward.


From the three plots of the input gates, we can observe that generally for stop words such as prepositions and articles the input gates have lower values, suggesting that the matching of these words is less important.

content words such as nouns and verbs tend to have higher values of the input gates, which also makes sense because these words are generally more important for determining the final relationship label.


(2006) about body words not providing additional value in their task classification work.

the GrausB(VP-TOI) in row (7) shows that using body terms more selectively has the potential for improving performance.


Syntax-based embeddings have been shown to have different properties in word similarity evaluations than their window based counterparts, better capturing the functional properties of words.

it is not clear if they provide any advantage for NLP tasks.


In genearal, we observe that the CNN performs better with Wavg, while SVM and LSTM with Conc.

the ensemble methods of the Win5 model (AvgE and ConcE) do not provide any consistent advantage over the baseline.


Therefore, the model actually splits the sentence locally into n-grams by sliding windows.

despite their ability to account for word orders, order-sensitive models based on neural networks still suffer from several disadvantages.


As the number of word embedding increases, this will increase the running time.

this tuning procedure is embarrassingly parallel.


Our model is similar in spirit to topic models: for an input dataset, the output of the RMN is a set of relationship descriptors (topics) and-for each relationship in the dataset-a trajectory, or a sequence of probability distributions over these descriptors (document-topic assignments).

the RMN uses recent advances in deep learning to achieve better control over descriptor coherence and trajectory smoothness (Section 4).


More specifically, the RMN and HTMM agree on environmental descriptions (e.g., boats, outdoors) and graphic sexual scenes ( Figure 5,  middle).

the RMN is more sophisticated with in-  terpersonal relationships.


This task was originally paired with a visual question answering task much simpler than the one just discussed, and is appealing for a number of reasons.

to the VQA dataset, GeoQA is quite small, containing only 263 examples.


Almost all these works focus on reducing the marginal distribution distance between different domain features in an unsupervised manner to make them indistinguishable.

considering a word is not evenly distributed conditioning on different labels, it may result in that the discriminative property of features from different domains may not be similar, which means that close source and target samples may not have the same label.


we conduct CRF parameter transfer by minimizing It turns out that a similar regularization term is applied in our CRF parameter transfer method and the regularization framework (RF) for domain adaptation (Lu et al., 2016).

rF is proposed to generalize the feature augmentation method in (Daume III, 2007), and these two methods are only discussed from a perspective of the parameter.


Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing.

our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context.


Current FETC systems sidestep the issue by either ignoring out-of-context labels or using simple pruning heuristics like discarding training examples with entities assigned to multiple types in the knowledge graph.

both strategies are inelegant and hurt accuracy.


To the best of our knowledge, the first use of a hierarchical loss function was originally introduced in the context of document categorization with support vector machines (Cai and Hofmann, 2004).

that work assumed that weights to control the hierarchical loss would be solicited from domain experts, which is inapplicable for FETC.


That is, the performance upper bounds of our proposed model are no longer 100%: for example, the best strict accuracy we can get in this setting is 88.28% for FIGER(GOLD).

as the strict accuracy of state-of-the-art methods are still nowhere near 80% (Table 3), the evaluation we perform is still informative.


For example, our model trained on D f iltered will misclassify S5 as Title, while the model trained on D raw can make the correct prediction.

there are still some errors that can't be fixed with our model.


For BREE and BRET, the definition follows directly from the fact that these are entity-pair and template-centered instantiations of BREX, respectively.

the disjunctive matching of instances for an extractor with entity pair and template seeds in BREJ (Figure 3 line "(i)" ) boosts the likelihood of finding positive instances.


In other words, config 9 (Table 5) is combination of both weighted negative and scaled positive extractions.

we also investigate ignoring w n p 1.0q in order to demonstrate the capability of BREJ with only scaling positives and without weighting negatives.


In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains.

to previous work, we work directly with the acoustic information-the first two formant values-rather than modeling discrete sets of phonemic symbols (IPA).


Also, we can notice that Wixarika has more unique words than the rest of our studied languages.

nahuatl has with 810 the highest number of unique morphemes.


MTT-U) is a single element in the input sequence (the one representing the task).

this information enables the model to handle each given instance correctly at inference time.


In the case of random strings, again, adding more training data seems to help more.

using corpus data seems to hurt performance and the more such examples we use, the worse accuracy we obtain.


For the similar problem of Chinese word segmentation, Zhang and Clark (2008) trained a model jointly on part-of-speech tagging.

we are not aware of any prior work on multi-task training or data augmentation for neural segmentation models.


Ruzsics and Samardzic (2017) extended the standard encoder-decoder architecture for canonical segmentation to contain a language model over segments and improved results.

a big difference to our work is that they still used more than ten times as much training data as we have available for the indigenous Mexican languages we are working on here.


It preserves information which is very close to what the decoder actually needs.

there might be some missing pieces of information or some incompatibility between the decoder and the table, so we do not freeze the morphology table during training, but let the decoder update it with respect to its needs in the forward and backward passes.


3 The main findings about the behaviour of the table are as follows: • The model assigns high attention weights to stem-C for almost all time steps.

the weights assigned to this class for t 1 and i 5 are much higher than those of affix characters (as they are part of the stem).


Translation information stored in the baseline decoder is not sufficient for selecting the right character 'G', so the decoder wrongly starts with 'i' and continues along a wrong path up to generating the whole word.

our decoder's information is accompanied with signals from the affix table which force it to start with a better initial character, whose sampling leads to generating the correct target word.


First, we note that adding any combination of acoustic-prosodic features (individually or in sets) improves performance over the text-only baseline.

certain combinations of acoustic-prosodic features are not always better than their subsets.


Our approach does not use a separate disfluency detection module; we hypothesized that the location-sensitive attention model helps handle these differences based on analysis of the text-only results (Table 1).

more explicit modeling of disfluency pattern match characteristics in a dependency parser (Honnibal and Johnson, 2014) leads to better disfluency detection performance (F = 84.1 vs. 76.7 for our text only model).


There were no pauses between all words in this sentence, the audio sample showed that the word own was both lengthened and raised in intonation, giving the prosody-enhanced parser (right) a signal that own is on a syntactic boundary.

the text-only parser (left) had no such information and made an NP-attachment error.


in the context of this conversation (the speaker was talking about another person in an informal manner), and everything acts more like filler -e.g.


2017, and the performance of the base model was comparable to the one reported.

we were unable to  reproduce the significant gains that were reported when using the reverse model (italicized in Table 3).


They used sequence-to-sequence models to transcribe Spanish speech and translate it in English, by jointly training the two tasks in a multitask scenario where the decoders share the encoder.

to our work, they use a large corpus for training the model on roughly 163 hours of data, using the Spanish Fisher and CALL-HOME conversational speech corpora.


We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause.

to existing datasets, we tackle the challenge of working with transcripts that derive from uncorrected closed captioning, using associated audio recordings to automatically extract and align labels for instances of audience applause.


The intuition behind our model is that addressing certain parts of the OH's reasoning often has little impact in changing the OH's view, even if the OH realizes the reasoning is flawed.

some parts of the OH's reasoning are more open to debate, and thus, it is reasonable for the model to learn and attend to parts that have a better chance to change an OH's view when addressed.


Challenger 1 addresses the OH's general statement and provides a new fact, which received a ∆.

challenger 2 addresses the OH's issue about race but failed to change the OH's view.


For most OH replies, the (non-)existence of a ∆ indicates whether a comment to which the OH replied changed the OH's view.

an OH's view is continually influenced as they participate in argumentation, and thus a ∆ given to a comment may not necessarily be attributed to the comment itself.


Moreover, prior work has mainly borrowed metrics from machine translation (MT) and paraphrase communities for evaluating style transfer.

it is not clear if those metrics are the best ones to use for this task.


To fill the gap, implicit discourse relation prediction has drawn significant research interest recently and progress has been made (Chen et al., 2016; by modeling compositional meanings of two discourse units and exploiting word interactions between discourse units using neural tensor networks or attention mechanisms in neural nets.

most of existing approaches ignore wider paragraph-level contexts beyond the two discourse units that are examined for predicting a discourse relation in between.


Consistent with results of previous works, neural tensors, when applied to Bi-LSTMs, improved implicit discourse relation prediction performance.

the performance on the three small classes (Comp, Cont and Temp) remains low.


This result indicates that the Pseudo-melody model uses the information of a given melody to make a better prediction of its lyrics word sequence.

the Heuristic model had the worst performance, despite training with a large amount of raw lyrics.


This result is consistent with the perplexity evaluation result.

regarding the "Grammaticality" and "Meaning" evaluation, workers gave high scores to the Lyrics-only and Pseudo-melody models that are well-trained on a large amount of text data.


These results indicate our pseudo data learning strategy contributes to generating high-quality lyrics.

the quality of lyrics automatically generated is still worse than the quality of lyrics that humans produce, and it still remains an open challenge for future research to develop computational models that generate high-quality lyrics.


Such frameworks enable a decoder to retrieve from a memory during generation.

less research has been done to take care of the memory contents from different sources, which are often of heterogeneous formats.


(2016) discuss different knowledge representations for a simple factoid QA task and show that classic structured KBs organized in a Key-Value Memory style work the best.

dealing with heterogeneous memory is not trivial.


Combining the two mechanisms to-gether gives HS-AttnHist the best performance in Enrichment.

hS-Attnhist still suffers from the repetition issue, to certain extent.


In recent work, Zhang and Lapata (2017) propose a reinforcement learning-based text simplification model which jointly models simplicity, grammaticality, and semantic fidelity to the input.

to these methods, Narayan and Gardent (2016)'s sentence simplification approach does not need a parallel corpus for training, but rather uses a deep semantic representation as input for simplification.


In Example 7.2, the top paraphrase identified by both AddCos-PPDB and AddCos-Simple PPDB for the word "monitor" is "track", which is a reasonable substitute.

in Example 7.3, AddCos-Simple PPDB model was able to identify a good simple substitute, when none of the other models were able to identify a suitable word with comparable complexity.


Previous works for factoid QG (Serban et al., 2016) claims to solve the issue of small size QA datasets.

encountering an unseen predicate / entity type will generate questions made out of random text generation for those out-of-vocabulary predicates a QG system had never seen.


General features: Most of the general features are based on (Attali and Burstein, 2006).

due to lack of tools for processing the Portuguese language, we implemented the following features, which are sub-divided as follows: Grammar and style: Features include the number of grammar errors and misspellings.


form document representations using citation relations, which are not available for unfinished or new documents.

our method does not need to be re-trained as the corpus of potential candidates grows.


That is, the textual embeddings for the query text and abstract share the same weights.

we had a significantly larger amount of data to train NNRank on OpenCorpus, and found that non-Siamese embeddings are beneficial.


We found that in the verification of answer-options, our annotations were in high agreement (98%) with those obtained from mechanical turk.

that was not the case for the verification of multi-sentence questions.


This method uses the top documents retrieved for each query to learn the association between the query terms and those occurring in the retrieved documents.

to retrieving ranked lists for every query as in (Zamani and Croft, 2017), we capture the semantic context of query words with the help of other useful cues for task-relatedness, e.g.


Informally speaking, the objective function aims to maximize the similarity between two word vectors w and v that are members of the same semantic context.

it minimizes the similarity between the word vector w and a word vector u randomly sampled from outside its context, as defined by the semantic relation S of Equation 2.


In order to compare our results with these studies, we use the same subset of 1424 queries from the AOL query log for the evaluation of task extraction effectiveness as used in these earlier studies.

since the purpose of these studies was only to extract tasks from a single session, in its annotation scheme two queries only qualified as part of the same task if they appeared within the same session.


The authors report F 1 scores of 23.9% and 56.6% for their definition extraction methods.

we assign meaning exclusively to variables, using denotations from a pre-computed dictionary of mathematical types, rather than freeform text.


The type dictionary distributed by Stathopoulos and Teufel (2016) contains 10,601 automatically detected types from the MREC.

the MREC contains 2.9 million distinct technical terms, many of which might also be types.


The TY/typed model yields almost double the MAP performance of its untyped counterpart (TY/untyped, .083 MAP).

the RT/typed and RT/untyped models perform comparably (no significant difference) but poorly.


Although learning paradigms like Transfer Learning (Pan and Yang, 2010) attempt to incorporate knowledge from one task into another, these techniques are limited in scalability and are specific to the task at hand.

humans have the intrinsic ability to elicit required past knowledge from the world on demand and infuse it with newly learned concepts to solve problems.


Incorporating Inductive Biases (Ridgeway, 2016) based on the known information about a domain onto the structure of the learned models, is an active area of research.

our motivation and approach is fundamentally different from these works.


The convolution based model, helped to reduce the space over entities and relationships over which attention had to be generated.

more sophisticated techniques using similarity based search (Wang et al., 2014a;Mu and Liu, 2017) can be pursued towards this purpose.


We can see that our fixnorm approach does not completely solve the mistranslation issue, since it translates Entoni Fauchi to UNK UNK (which is arguably better than James Chan).


As we can see in Figure 2, because of the alignment shift, both tied and fixnorm incorrectly replace the two unknown words (in bold) with But Deutsche instead of Deutsche Telekom.

under fixnorm+lex and the model of Arthur et al.


Some have focused on reducing the number of UNKs by enabling NMT to learn from a larger vocabulary (Jean et al., 2015;Mi et al., 2016); others have focused on replacing UNKs by copying source words (Gulcehre et al., 2016;Gu et al., 2016;Luong et al., 2015b).

these methods only help with unknown words, not rare words.


With such large systems, NMT showed that it can scale up to immense amounts of parallel data in the order of tens of millions of sentences.

such data is not widely available for all language pairs and domains.


Alternatively, we could train monolingual embeddings in a shared space and use these as the input to our MT system.

since these embeddings are trained on a monolingual objective, they will not be optimal for an NMT objective.


For instance, the influence can be approximately ranked as Es ≈ P t > F r ≈ It > Cs ≈ El > De > F i, which is interestingly close to the grammatical relatedness of Ro to these languages.

cs has a strong influence although it does not fall in the same language family with Ro, we think this is due to the geo-graphical influence between the two languages since cs and Ro share similar phrases and expressions.


1 Sequence to sequence models are usually trained with a simple token-level likelihood loss Bahdanau et al., 2014).

at test time, these models do not produce a single token but a whole sequence.


Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors.

their ability to model corpora are limited by the representation and selection of these featuresa choice the topic modeler must make.


We calculated this topic quality metric on the top 20 most probable words in each topic, and averaged over the most coherent 1, 5, 10, and over all learned topics.

models were selected to only maximize average NPMI over all topics.


Model fitting is performed by back-propagation of a max-margin cost.

we use neural networks to learn feature representations for documents, not as a replacement for the LDA generative story.


Sample topic and discourse assignments z and d at the message level and word type switcher x at the word level, using the distributions, computed according to parameters optimized in step 1: Step 2 is analogous to Gibbs Sampling (Griffiths, 2002) in probabilistic graphical models, such as LDA (Blei et al., 2003).

distinguishing from previous models, the multinomial distributions in our models are not drawn from a Dirichlet prior.


(2014) examined principal roles in 80 discussions from the Wikipedia: Article for Deletion pages (focusing on stubbornness or ignoredness, among others) and found several typical roles, including 'rebels', 'voices', or 'idiots'.

to our data under investigation (Change My View debates), Wikipedia talk pages do not adhere to strict argumentation rules with manual moderation and have a different pragmatic purpose.


This corresponds to observations made in comments in newswire where 'weightier' topics tended to stir incivility (Coe et al., 2014).

'stupidity' (or 'reasonableness') does not seem to play any significant role.


This design ensures that an arc is associated with every word.

in our setting for scene graph generation, there may be no arc for some of the words, especially empty words.


Neutrality is comparably easier to achieve by reusing terms in the whole set of targets as decoys.

a decoy may hardly meet QoU and IoU simultaneously 1 .


describe-01 he curmudgeon she ARG0 ARG2 ARG1 Figure 1: AMR graph for "He described her as a curmudgeon", "His description of her: curmudgeon" and "She was a curmudgeon, according to his description" should result in the same AMR graph as shown in Figure 1.


As such, one cannot expect to use AMR in the transparent way mentioned above to identify paraphrase relations.

we demonstrate in this paper that AMR can be used in a "softer" way to detect such relations.


(2016), which learns word representations by adopting a ranking-based loss function.

none of these models includes any contextual information beyond the neighbouring words.


The performance of the CNN model improves if POS vectors are considered together with GloVe word vectors in input, both when such POS vectors are randomly initialized ( w i p r ) and independently trained with the GloVe model ( w i p i ).

the best performance is achieved when word and POS tags vectors are jointly trained with our attr2vec model ( w j p j ).


Thus, these are time points that the model perceives as having increased semantic change.